OpenAI Interview Guide

Process, patterns, and how to prepare effectively

OpenAI's interviews are not just "grind LeetCode and hope for the best." They tend to be:

Open-ended – framed around realistic problems rather than toy puzzles
Follow-up heavy – the real evaluation happens in the follow-up questions
Depth-focused – if you claim expertise in something, they go deep

This guide summarizes the OpenAI interview process, what interviewers look for, and how to prepare in a focused way. Whether you're targeting a software engineering role or an ML-focused position, understanding OpenAI's unique interview style is crucial for success.

1. OpenAI Interview Process Overview

The exact loop varies by team and role, but a typical OpenAI software/ML engineer process looks like:

1. Recruiter Screen

Background, experience, what you're looking for
Overview of the role and team
High-level alignment check

2. Tech Screen (2 rounds)

One Coding round
One System Design round

For more ML-heavy roles, these may become:

ML Coding instead of standard coding
ML Design instead of generic system design

3. Virtual Onsite (3–4 rounds)

Usually includes:

System Design (or ML Design)
Coding (or ML Coding)
Project Deep Dive
- You present one or two past projects using slides
- Interviewers probe architecture, tradeoffs, failures, metrics
Behavioral / Culture fit
- Example topics: Discussion about your views on AGI and AI Safety
- Standard conflict / leadership / ownership questions
- Generally straightforward; not designed as a "gotcha" round, but shallow answers still hurt

4. Hiring Manager (some candidates)

Not everyone gets this round
Focus on team fit, expectations, roadmap, and how you'd be positioned in the team
May include light technical or scenario questions

That's the backbone of the OpenAI interview process you should plan around.

2. Round-by-Round Breakdown

2.1 Recruiter Screen

What to expect:

Walkthrough of your recent experience
Why you're interested in OpenAI and this role
What kind of work/teams you're targeting (infra, product, safety, applied, research, etc.)

How to prepare:

Be able to summarize your last 1–2 roles in 2–3 sentences each
Have a clear, specific answer to "Why OpenAI?" and "Why this role/team?"

2.2 Tech Screen (Coding + System Design / ML Design)

Coding (or ML Coding)

What to expect:

Clarifying the problem
Choosing reasonable approaches
Writing correct, clean code
High bar for correctness – they expect strong solutions given the competitive candidate pool
Follow-up questions after solving the basic problem
Handling edge cases and test cases proactively

System Design (or ML Design)

Interviewers look for:

Clear problem decomposition
Reasonable architecture and tradeoffs
How your design evolves under new constraints (scale, latency, cost, reliability)

2.3 Virtual Onsite

System Design / ML Design

Deeper and more open-ended than the screen
Expect multiple rounds of follow-up:
- "What if traffic grows 100x?"
- "What if we prioritize latency over cost?"
- "What breaks first, and how do you know?"

Aim to show:

Structured thinking
Awareness of tradeoffs
Ability to iterate on your own design calmly

Coding / ML Coding

Similar flavor to the screen, but may be more complex or have additional constraints

Project Deep Dive (with slides)

This is a key round.

What to expect:

You prepare a slide deck about a significant project
You present:
- Problem context and goals
- Constraints and success metrics
- Architecture and key decisions
- Performance/reliability considerations
- Failures, incidents, and what you learned
Interviewers then deep dive:
- "Why this design?"
- "What were the main tradeoffs?"
- "What went wrong in production?"
- "How did you measure success?"

How to prepare:

Pick a project that shows meaningful ownership
Practice presenting and handling potential questions from the interviewer

Behavioral / Culture

What to expect:

Discussion of:
- Your views on AI (e.g., benefits, risks, timelines, responsible deployment)
- Your thoughts on AI Safety (what matters, what tradeoffs you see)
Standard behavioral questions about:
- Conflict with teammates
- Leading projects or initiatives
- Handling failure, feedback, and uncertainty

This round is usually not "trick-heavy" but shallow or inconsistent answers can hurt you.

How to prepare:

Prepare 4–6 concrete stories:
- A difficult project
- A conflict and how you resolved it
- A failure/incident and what you learned
- A time you took ownership and drove something forward
Think through:
- What you hope AI will be used for
- Risks or failure modes you care about
- How to balance innovation and safety in your work

2.4 Hiring Manager

Not everyone gets this round, but when it happens:

Expect discussions about:

The team's current and upcoming projects
Your role, scope, and growth path
How you like to work and what you're optimizing for
There may be light technical or scenario questions, but the focus is fit and expectations

How to prepare:

Be honest about what kind of work and environment you want
Prepare thoughtful questions about the team, roadmap, and collaboration style

3. Role Variations: SWE vs ML-Focused Roles

The core structure is similar, but for more ML-focused roles:

Coding → ML Coding

More data/ML-flavored problems
Some emphasis on numerical stability, metrics, evaluation logic, etc.

System Design → ML Design

ML training and serving pipelines
Feature/data flow, labeling, feedback loops
Evaluation frameworks and experimentation

Regardless of the flavor, OpenAI expects solid engineering fundamentals and the ability to reason about systems, not just models in isolation.

4. How to Prepare Efficiently

The Traditional Approach Doesn't Work

Many candidates spend months grinding random LeetCode problems or watching generic system design videos, only to be caught off-guard by OpenAI's specific interview style and questions. The problem? OpenAI draws from a relatively small, rotating set of questions – and those questions are quite different from typical FAANG interviews.

A More Strategic Approach

Instead of studying 1,000+ random problems, focus on:

Real OpenAI questions – Practice with verified questions from recent interviews
Depth over breadth – Master the patterns and follow-up variations
Complete preparation – Combine technical prep with project stories and AI safety perspectives

Ready to practice? Check out our OpenAI interview questions.

Process, patterns, and how to prepare effectively

OpenAI's interviews are not just "grind LeetCode and hope for the best." They tend to be:

Open-ended – framed around realistic problems rather than toy puzzles
Follow-up heavy – the real evaluation happens in the follow-up questions
Depth-focused – if you claim expertise in something, they go deep

1. OpenAI Interview Process Overview

The exact loop varies by team and role, but a typical OpenAI software/ML engineer process looks like:

1. Recruiter Screen

Background, experience, what you're looking for
Overview of the role and team
High-level alignment check

2. Tech Screen (2 rounds)

One Coding round
One System Design round

For more ML-heavy roles, these may become:

ML Coding instead of standard coding
ML Design instead of generic system design

3. Virtual Onsite (3–4 rounds)

Usually includes:

System Design (or ML Design)
Coding (or ML Coding)
Project Deep Dive
- You present one or two past projects using slides
- Interviewers probe architecture, tradeoffs, failures, metrics
Behavioral / Culture fit
- Example topics: Discussion about your views on AGI and AI Safety
- Standard conflict / leadership / ownership questions
- Generally straightforward; not designed as a "gotcha" round, but shallow answers still hurt

4. Hiring Manager (some candidates)

Not everyone gets this round
Focus on team fit, expectations, roadmap, and how you'd be positioned in the team
May include light technical or scenario questions

That's the backbone of the OpenAI interview process you should plan around.

2. Round-by-Round Breakdown

2.1 Recruiter Screen

What to expect:

Walkthrough of your recent experience
Why you're interested in OpenAI and this role
What kind of work/teams you're targeting (infra, product, safety, applied, research, etc.)

How to prepare:

Be able to summarize your last 1–2 roles in 2–3 sentences each
Have a clear, specific answer to "Why OpenAI?" and "Why this role/team?"

2.2 Tech Screen (Coding + System Design / ML Design)

Coding (or ML Coding)

What to expect:

Clarifying the problem
Choosing reasonable approaches
Writing correct, clean code
High bar for correctness – they expect strong solutions given the competitive candidate pool
Follow-up questions after solving the basic problem
Handling edge cases and test cases proactively

System Design (or ML Design)

Interviewers look for:

Clear problem decomposition
Reasonable architecture and tradeoffs
How your design evolves under new constraints (scale, latency, cost, reliability)

2.3 Virtual Onsite

System Design / ML Design

Deeper and more open-ended than the screen
Expect multiple rounds of follow-up:
- "What if traffic grows 100x?"
- "What if we prioritize latency over cost?"
- "What breaks first, and how do you know?"

Aim to show:

Structured thinking
Awareness of tradeoffs
Ability to iterate on your own design calmly

Coding / ML Coding

Similar flavor to the screen, but may be more complex or have additional constraints

Project Deep Dive (with slides)

This is a key round.

What to expect:

You prepare a slide deck about a significant project
You present:
- Problem context and goals
- Constraints and success metrics
- Architecture and key decisions
- Performance/reliability considerations
- Failures, incidents, and what you learned
Interviewers then deep dive:
- "Why this design?"
- "What were the main tradeoffs?"
- "What went wrong in production?"
- "How did you measure success?"

How to prepare:

Pick a project that shows meaningful ownership
Practice presenting and handling potential questions from the interviewer

Behavioral / Culture

What to expect:

Discussion of:
- Your views on AI (e.g., benefits, risks, timelines, responsible deployment)
- Your thoughts on AI Safety (what matters, what tradeoffs you see)
Standard behavioral questions about:
- Conflict with teammates
- Leading projects or initiatives
- Handling failure, feedback, and uncertainty

This round is usually not "trick-heavy" but shallow or inconsistent answers can hurt you.

How to prepare:

Prepare 4–6 concrete stories:
- A difficult project
- A conflict and how you resolved it
- A failure/incident and what you learned
- A time you took ownership and drove something forward
Think through:
- What you hope AI will be used for
- Risks or failure modes you care about
- How to balance innovation and safety in your work

2.4 Hiring Manager

Not everyone gets this round, but when it happens:

Expect discussions about:

The team's current and upcoming projects
Your role, scope, and growth path
How you like to work and what you're optimizing for
There may be light technical or scenario questions, but the focus is fit and expectations

How to prepare:

Be honest about what kind of work and environment you want
Prepare thoughtful questions about the team, roadmap, and collaboration style

3. Role Variations: SWE vs ML-Focused Roles

The core structure is similar, but for more ML-focused roles:

Coding → ML Coding

More data/ML-flavored problems
Some emphasis on numerical stability, metrics, evaluation logic, etc.

System Design → ML Design

ML training and serving pipelines
Feature/data flow, labeling, feedback loops
Evaluation frameworks and experimentation

Regardless of the flavor, OpenAI expects solid engineering fundamentals and the ability to reason about systems, not just models in isolation.

4. How to Prepare Efficiently

The Traditional Approach Doesn't Work

A More Strategic Approach

Instead of studying 1,000+ random problems, focus on:

Real OpenAI questions – Practice with verified questions from recent interviews
Depth over breadth – Master the patterns and follow-up variations
Complete preparation – Combine technical prep with project stories and AI safety perspectives

Ready to practice? Check out our OpenAI interview questions.